Abstract

In this paper a novel inter class clustering method is developed for image reconstruction. The aim of image restoration is the removal of noise from images. The simplest possible approach for noise removal is various types of filters such as low-pass filters or median filters. More sophisticated methods assume a model of how the local image structures look like, a model which distinguishes them from the noise. The proposed method analyzed the image data in terms of the local image structures, such as lines or edges, and then controlling the filtering based on local information from the analysis, a better level of noise removal is usually obtained compared to the simpler approaches. The fuzzy entropic technique produced better results for simple and synthetic images and retains the important features in multiresolution images and extracting the features effectively, but it fails to stumpy edged and noisy images. To overcome this limitation here inter class clustering method is proposed for reconstruction. The experimental result shows that the proposed method has produce better results over the entropic and fuzzy classification methods.

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